Single-Source Generalization for Cross-Geospatial Object Detection

Cross-geospatial shifts in remote-sensing imagery cause large drops in detector performance. These shifts are compound, as environmental conditions and scene characteristics vary together across regions. Single-source domain generalization for object detection (SDGOD) has been extensively studied under weather and illumination shifts but remains under-examined for such compound shifts. To bridge this gap, we propose CoDAlign, which combines factor-guided composite augmentation with foreground-decoupled classification alignment. First, we organize source-image augmentation around explicit geographic appearance factors and validate the induced changes in an image-anchored factor space. The same factor axes enable controlled diagnostics of the robustness gained through augmentation and the shifts that remain challenging. Second, we identify how low foreground confidence in augmented images weakens class consistency in full-distribution prediction alignment. Our decoupled alignment strengthens correct-class consistency at regions matched to ground-truth objects while retaining foreground-background and non-target class alignment. Experiments across cross-region benchmarks and multiple detector architectures demonstrate that CoDAlign consistently outperforms existing SDGOD methods.